Writing a Python list of lists to a CSV file

python

csv-writing

pandas

data-structures

byAnton Shumikhin·Mar 14, 2025

To swiftly convert a list of lists into a CSV file, we lean on Python's mighty csv.writer:

import csv

data = [['Header1', 'Header2'], ['Row1Col1', 'Row1Col2'], ['Row2Col1', 'Row2Col2']]

# Pop open a file (popping champagne is optional but encouraged)
with open('pop_the_cork.csv', 'w', newline='') as f:
    csv.writer(f).writerows(data)

Voila! Don't sleep on simplicity. This one-liner deposits your list of lists into a CSV file with parallel lines of headers and rows.

Customizing your CSV output

Like a bespoke suit, sometimes you need your CSV to fit just right. Customizing the delimiter and the quotes? Snazzy!

with open('tailor_made.csv', 'w', newline='') as f:
    writer = csv.writer(f, delimiter=';', quotechar='"', quoting=csv.QUOTE_MINIMAL)
    writer.writerows(data)

Who needs a comma when a semicolon will do? Set your own rules and quote char, ride on Python's back, not the other way around.

Juggling data types

Despite how it sounds, csv.writer() is not the new circus act in town. It's your trusty companion that grapples with data types so you don't have to:

mixed_data = [['ID', 'Value', 'Status'], [1, 43.5, 'Active'], [2, 38.0, 'Inactive']]

with open('juggle_freely.csv', 'w', newline='') as f:
    csv.writer(f).writerows(mixed_data)

Leave excessive type-casting to low-level languages; enjoy Python's elegant handling of ints, floats, and strings in harmony.

Using pandas for extra oomph

For industrial-scale projects or complicated data manipulations, bring in the Pandas:

import pandas as pd

# Here be data
pd_data = pd.DataFrame(data)

# Release the Pandas!
pd_data.to_csv('pandas_are_loose.csv', index=False, header=False)

Pandas: because sometimes, you need to "Unleash the beast" (queue catchy guitar riff). This beast, a DataFrame, is a versatile data structure that can handle complex data manipulations, scaling heights regular lists can only dream of. And, it writes CSV files too.

Crafting the structure of your CSV with pandas

Thumbing your nose at the lack of headers on a list of lists? Laugh in the face of excluded rows. With Pandas, it's a breezy afternoon in customization land:

headers = ['Product', 'Price']
rows = [['Book', 9.99], ['Pen', 0.99]]

# Who says you can't have your data and eat it too?
df = pd.DataFrame(rows, columns=headers)
df.to_csv('made_to_order.csv', index=False)

So, go on, add headers if you want to. On the other hand, kick them out if you feel like it. It's your world.

Sidestepping common CSV pitfalls

Like a banana peel in a comedy sketch, incorrect file handling could make you slip. So ensure you always open CSV files with newline=''.

Turbocharging file handling

Working with large amounts of data? Don't watch paint dry. Trust in the speed of Pandas.

When crunching numbers in-memory, the arrays in NumPy chew through data like a hot knife through butter.

Maintaining robust code

Rome wasn't built in a csv.writer(), sometimes you need to loop:

with open('data_loop.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    
    # "Life is short, use Python" - Confucius
    for sublist in data:
        writer.writerow(sublist)